Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782753
Title: Pleiotropy in complex traits
Author: Hackinger, Sophie
ISNI:       0000 0004 7968 3552
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2019
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Abstract:
Genome-wide association studies (GWAS) have uncovered thousands of complex trait loci, many of which are associated with multiple phenotypes. The dedicated study of these pleiotropic effects is becoming increasingly common due to the availability of sample collections with high-dimensional phenotype data, such as the UK Biobank, and can yield important insights into the aetiology underlying complex disorders. In my PhD, I performed multi-trait analyses of medically relevant complex phenotypes to identify shared genetic factors. My first project involved a genome-wide overlap analysis of osteoarthritis (OA) and bone-mineral density (BMD), using summary statistics from two large-scale GWAS. OA and BMD are known to be inversely correlated, yet the genetics underlying this link remain poorly understood. I found robust evidence for association with OA at the SMAD3 locus, which is known to play a role in bone remodeling and cartilage maintenance. My second project aimed to elucidate the increased prevalence of type 2 diabetes (T2D) in schizophrenia (SCZ) patients. I used GWAS summary statistics of SCZ and T2D from the PGC and DIAGRAM consortia, respectively, to perform polygenic risk score analyses in three patient groups (SCZ only, T2D only, comorbid SCZ and T2D) and population-based controls. I find that the comorbid patient group have a higher genetic risk for both T2D and SCZ compared to controls, supporting the hypothesis that the epidemiologic link between these disorders is at least in part due to genetic factors. In my third project, I leveraged the correlation structure of over 274 protein biomarkers and 57 quantitative traits to perform multivariate GWAS on correlated trait clusters in a Greek isolated population. This approach uncovered several novel cis-associations not identified in single-trait GWAS, and highlights the power advantage of multivariate analysis. An important consideration for future studies will be the interpretation and follow-up of cross-phenotype associations, and the translation of these insights into clinical use.
Supervisor: Zeggini, Eleftheria Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.782753  DOI:
Keywords: human genetics ; pleiotropy ; association study ; gwas ; multi-trait
Share: